Multi-Modality and Temporal Analysis of Cervical Cancer Treatment Response

TL;DR

This study explores how combining multi-modal imaging and texture analysis over time can improve prediction of cervical cancer treatment response, potentially leading to more personalized and effective therapies.

Contribution

It demonstrates the effectiveness of multi-modal imaging and GLCM texture features, especially contrast, in predicting treatment outcomes for cervical cancer.

Findings

Contrast is the most predictive GLCM feature.

Multi-modal imaging enhances prognostic accuracy.

Potential to reduce measurement modalities and time points.

Abstract

Cervical cancer presents a significant global health challenge, necessitating advanced diagnostic and prognostic approaches for effective treatment. This paper investigates the potential of employing multi-modal medical imaging at various treatment stages to enhance cervical cancer treatment outcomes prediction. We show that among Gray Level Co-occurrence Matrix (GLCM) features, contrast emerges as the most effective texture feature regarding prediction accuracy. Integration of multi-modal imaging and texture analysis offers a promising avenue for personalized and targeted interventions, as well as more effective management of cervical cancer. Moreover, there is potential to reduce the number of time measurements and modalities in future cervical cancer treatment. This research contributes to advancing the field of precision diagnostics by leveraging the information embedded in noninvasive medical images, contributing to improving prognostication and optimizing therapeutic strategies for individuals diagnosed with cervical cancer.